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Comparative Study
. 2008 Mar;19(3):547-58.
doi: 10.1681/ASN.2007040469. Epub 2008 Jan 30.

The local and systemic inflammatory transcriptome after acute kidney injury

Dmitry N Grigoryev  1 , Manchang Liu, Heitham T Hassoun, Chris Cheadle, Kathleen C Barnes, Hamid Rabb
Affiliations
Comparative Study

The local and systemic inflammatory transcriptome after acute kidney injury

Dmitry N Grigoryev et al. J Am Soc Nephrol. 2008 Mar.

Abstract

Studies in humans and animal models have demonstrated that acute kidney injury (AKI) has a significant effect on the function of extrarenal organs. The combination of AKI and lung dysfunction is associated with 80% mortality; the lung, because of its extensive capillary network, is a prime target for AKI-induced effects. The study presented here tested the hypothesis that AKI leads to a vigorous inflammatory response and produces distinct genomic signatures in the kidney and lung. In a murine model of ischemic AKI, prominent global transcriptomic changes and histologic injury in both kidney and lung tissues were identified. These changes were evident at both early (6 h) and late (36 h) timepoints after 60-min bilateral kidney ischemia and were more prominent than similar timepoints after sham surgery or 30 min of ischemia. The inflammatory transcriptome (109 genes) of both organs changed with marked similarity, including the innate immunity genes Cd14, Socs3, Saa3, Lcn2, and Il1r2. Functional genomic analysis of these genes suggested that IL-10 and IL-6 signaling was involved in the distant effects of local inflammation, and this was supported by increased serum levels of IL-10 and IL-6 after ischemia-reperfusion. In summary, this is the first comprehensive analysis of concomitant inflammation-associated transcriptional changes in the kidney and a remote organ during AKI. Functional genomic analysis identified potential mediators that connect local and systemic inflammation, suggesting that this type of analysis may be a useful discovery tool for novel biomarkers and therapeutic drug development.

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Figures

Figure 1.
Figure 1.
Effect of kidney ischemia-reperfusion injury on renal function. Renal function pre- (0 h) and postischemia reperfusion was evaluated measuring serum creatinine (SCr) concentration. All mice underwent sham operation, 30- or 60-min bilateral renal ischemia followed by reperfusion, and serum creatinine (SCr) was measured at 0 (before), 6, and 36 h after surgery. All three groups of mice had comparable baseline creatinines. Compared with sham, SCr was not different at 6 h after 30 min renal ischemia, and at 36 h approximately half of the tested mice demonstrated an increase in SCr. Sixty-minute renal ischemia led to a significant increase in SCr at 6 h and even higher levels of SCr at 36 h after ischemia. (*P < 0.002 versus corresponding sham-operated mice; **P < 0.0001 versus corresponding sham-operated mice; n = 3 to 5 per group).
Figure 2.
Figure 2.
Histology of kidney tissues from mice after sham operation, 30- or 60-min renal ischemia and transcriptional contribution of leukocytes to kidney tissue transcriptomics. (A) 30-min sham, (B) 60-min sham, (C) 30-min ischemia and 6-h reperfusion, (D) 60-min ischemia and 6-h reperfusion, (E) 30-min ischemia and 36 h reperfusion, and (F) 60-min ischemia and 36-h reperfusion. Comparing sham kidney with normal histology, both 30- and 60-min ischemic kidneys displayed cast formation and tubular injury at 6- or 36-h postischemia. Sixty-minute ischemic kidney had more injury than 30-min ischemia at each time point. Hemotoxylin and eosin stain. Arrows indicate injured tubules. Open arrows indicate debris and cast formation. (G and H) Transcriptional changes of leukocyte-specific Cd11 gene. Compared with the sham operated controls, the overall contribution of leukocytes to kidney transcriptomics is moderate (5 to 30%). IRI, ischemia reperfursion injury.
Figure 3.
Figure 3.
Histology of lung tissues from mice after sham operation, 30- or 60-min renal ischemia, and transcriptional contribution of leukocytes to lung tissue transcriptomics. (A) 30-min sham, (B) 60-min sham, (C) 30-min ischemia and 6-h reperfusion, (D) 60-min ischemia and 6-h reperfusion, (E) 30-min ischemia and 36-h reperfusion, and (F) 60-min ischemia and 36-h reperfusion. Compared with the sham-operated controls, 30-min renal ischemia induced mild lung changes with septal edema and hypercellularity at 6 h, which resolved by 36-h postischemia. Meanwhile, 60-min renal ischemia induced lung changes at 6 h that persisted throughout the 36-h reperfusion period. (G and H) Moderate (10 to 50%) transcriptional changes of Cd11 gene with the highest leukocyte contribution to lung tissue transcriptomics at 36 h after 60 min of kidney ischemia.
Figure 4.
Figure 4.
Detection of AKI candidate genes affected by both 30- and 60-min ischemia in kidney and lung tissues. Gene expression profiles of IRI-affected kidney tissues (control = 3, IRI = 3) were analyzed using GeneChip Operating Software (GCOS) 1.4 and the Significance Analysis of Microarrays algorithm, and genes with a false discovery rate (FDR) < 1% and ± 2.85 changes in expression were considered significantly affected by IRI. Severity-independent inflammatory candidate genes were selected by crossreferencing significantly IRI-affected genes after 30- and 60-min exposure to ischemia and a recovery period of 6 or 36 h in (A) kidney or (B) lung tissues. Dashed and solid circles represent 30- and 60-min ischemia, respectively.
Figure 5.
Figure 5.
Hierarchical clustering of inflammatory genes affected by 6- or 36-h exposure to IRI induced by 30- or 60-min of ischemia. (A) The 102 kidney inflammatory genes that were significantly affected by IRI after 30 or 60 min of ischemia were combined and clustered using MeV software. Fold change values (log2) were calculated by subtracting the average of their corresponding controls (n = 3) from individual gene expression value of each biological replicate. The clustering was conducted based on the gene expression pattern rather than amplitude using uncentered Pearson correlation and applying an average linkage algorithm, which identified five major clusters (more than five genes, blue triangles). The expression pattern of the well-known kidney injury-related gene that codes for neutrophil gelatinase-associated lipocalin (Lcn2) is highlighted with a white rectangle and identified in the gene list with a dotted arrow. (B) The 94 lung inflammatory genes that were significantly affected by kidney IRI after 30 or 60 min of ischemia were combined and clustered as described above. The six major clusters (blue triangles) were identified, of which Lcn2-containing clusters (bottom blue rectangle) demonstrated upregulation throughout all conditions. Genes from most representative clusters are listed on the right. Each column represents an experimental condition of ischemia-affected kidney or lung sample and each row represents an expression pattern of a gene throughout given experimental conditions. Red color indicates upregulation and green color indicates downregulation of gene expression relative to corresponding controls, with color intensity corresponding to the fold-change amplitude (fold-change scale shown on the left).
Figure 6.
Figure 6.
Inflammatory signature identified by hierarchical clustering of kidney and lung gene expression profiles. The 109 inflammatory genes that were significantly affected in kidney, lung, or both tissues were normalized, and unsupervised clustering was performed simultaneously for the kidney and lung samples. In addition to our in-house normalization procedure the original signal intensity values were also processed using conventional Cluster software. The “Adjust Data” function was applied to kidney and lung expression profiles using log transformation and mean center normalization of genes and arrays. The normalized gene expression profiles for each tissue were combined; inflammatory genes were extracted and clustered using uncentered Pearson correlation (average linkage). Each row represents an experimental condition including shams and is labeled on the right (K, kidney; L, lung; 30IRI, 30-min ischemia; 60IRI, 60-min ischemia; 1, 2, or 3, biologic replicate). Each column represents the expression pattern of a gene throughout given experimental conditions and the five most representative genes are marked with the arrows at the bottom. Each gene expression value is normalized to the mean column expression value of a given gene throughout all experimental conditions. The resulting values are color-coded as higher than column mean (yellow) or lower than column mean (blue), where color intensity corresponds to the amplitude of deviation from the mean (deviation scale is shown on the left). Three major clusters that represent sham, short exposure to ischemia, and long exposure to ischemia are separated in individual blocks. Seven significant (three or more members) clusters represented by large blue triangles on the sample tree are bracketed and named on the right. The location of candidate genes from Figure 7 is marked with arrows and corresponding gene symbols.
Figure 7.
Figure 7.
Expression level of inflammatory candidate genes in AKI-affected kidney and lung tissues detected by real-time reverse transcriptase PCR (rtPCR) and Affymetrix GeneChip. The Affymetrix expression values for five inflammatory genes that are listed on the x-axis were generated by hybridization of total mouse RNA isolated from kidney (n = 3, top panel) or lung (n = 3, bottom panel) to MOE430A GeneChip. The transcriptional changes were identified by comparing each gene's expression value to the mean of corresponding controls (n = 3) and expressed as mean of fold changes (open bars) where error bars represent SD. Transcriptional changes in all but two samples were statistically significant (FDR < 1%, FCkidney > 2.85, or FClung > 2.38). The relative message abundance in the same set of samples was evaluated by real-time rtPCR and compared with internal controls (three housekeeping genes) as described in Concise Methods. The fold changes in transcript abundance were computed and expressed as mean average fold change versus corresponding controls (solid bars). The error bars represent SD. Changes in transcript abundance were statistically significant (P < 0.05) in all rtPCR samples. ≠, microarray-identified fold changes failed significance testing according to power-predicted parameters described in Concise Methods. Lcn2, lipocalin 2; Socs3, suppressor of cytokine signaling 3; Saa3, serum amyloid A 3; Il1r2, IL-1 receptor, type II; Cd14, CD14 antigen.
Figure 8.
Figure 8.
Global functional analysis. The significance value associated with a function in global analysis is a measure for how likely a group of our candidate genes is involved in represented (x-axes) function. The significance is expressed as a P value that is calculated using the right-tailed Fisher's exact test and represented as negative log value (y-axis). The threshold line represents significant P value 0.05 (1.3 in −log units).
Figure 9.
Figure 9.
Representative chemokine/cytokine protein levels in mouse serum 6 and 36 h after IRI. Cytokines were measured with a protein multiple Bioplex technique in serum from mice that underwent sham surgery (open bars) or IRI (solid bars) and sacrificed at 6 or 36 h after surgery. (*P < 0.05 and **P < 0.005 versus corresponding sham-operated groups).
Figure 10.
Figure 10.
Flow chart of Affymetrix gene expression data processing (electronic masking of oligonucleotide probes, background adjustment, and chip intensity normalization). The rectangular boxes represent data sets generated and used by the algorithm. Oval shapes depict steps at which manipulations with the data were conducted. Rhomboids represent the steps at which decision is made. The digitized hybridization signals were analyzed on a probe level using Bioconductor (www.bioconductor.org) affy package. Probes that produced detectable hybridization signal in less than 5% of kidney or lung sample hybridizations were considered nonfunctional and combined into tissue specific mask (*.MSK) files. The subsequent electronic masking of nonfunctional probes was conducted during GCOS 1.4 expression data re-evaluation as described previously. The process of identification of the background hybridization signal was modified from a previously reported approach. Briefly, the quartiles of highest hybridization signals among all “Absent” calls (P > 0.04) from each chip were averaged and considered a nonspecific (background) hybridization signal of a given chip. The average of all “Absent” values was considered an array brightness coefficient for chip normalization. The expression data were stratified by experimental conditions (n = 3) and hybridization of each transcript was evaluated. The transcripts that were called “Present” by the GCOS 1.4 algorithm and produced signal at least twice as high as that of background in at least two of three hybridizations in any given cluster were considered tissue-specific. The signal intensity values of these transcripts from each chip were increased by corresponding to a given chip background value (background adjustment) and divided by a chip brightness coefficient (normalization). All of the data manipulation processes were written in Python 2.2 (www.python.org) and scripts are available upon request.
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